爱尔兰土壤成分与肌萎缩性侧索硬化症相对风险之间无关联。
No association between soil constituents and amyotrophic lateral sclerosis relative risk in Ireland.
机构信息
Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College, Dublin 2, Ireland.
Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College, Dublin 2, Ireland.
出版信息
Environ Res. 2016 May;147:102-7. doi: 10.1016/j.envres.2016.01.038. Epub 2016 Feb 6.
INTRODUCTION
We have recently mapped ALS spatial risk in Ireland using Bayesian and cluster analysis methods at electoral division (ED) and small area (SA) levels. As a number of metal elements (both minerals and toxins) have been proposed as risk factors for ALS, here we extend this analysis to include soil constituents from the Irish National Soils Database as Bayesian conditional auto-regression covariates to determine associations with small area ALS risk.
METHODS
Data on 45 different soil parameters were obtained under license from National Soils Database (via Irish EPA). We interpolated average values of each soil constituent for each small area using ordinary kriging. All cases of ALS in Ireland from January 1995 to December 2013 were identified from the Irish ALS register and observed and age and gender standardised expected cases were calculated for each SA. Besag-York-Mollié (BYM) models were then built including each parameter from the national soils database in turn as a Bayesian covariate in the BYM model. Models were compared using the deviance information criterion (DIC) and separate models were built for ALS subtypes.
RESULTS
1701 ALS patients were included - 959 (56%) were male, 938 (55%) had limb onset ALS. 315 Bayesian models were built in total. Of the 315 models built, only one resulted in a coefficient that did not overlap zero. For limb onset cases, total magnesium had a mean coefficient of 0.319 (credible interval 0.033-0.607).
DISCUSSION
We report the first spatial analysis of potential association between ALS and soil minerals using a population-based dataset collected over 18 years. Our sole non-zero finding is likely a random finding due to the high number of models built. We did not find any evidence to support soil mineral and toxin levels as risk factors for ALS. However as soil parameters are an ecological assessment of exposure in a given area, individual level measures of exposure are required.
介绍
我们最近使用贝叶斯和聚类分析方法在选举分区（ED）和小区域（SA）水平上绘制了爱尔兰 ALS 空间风险图。由于许多金属元素（包括矿物质和毒素）被认为是 ALS 的危险因素，因此我们在此将分析扩展到包括爱尔兰国家土壤数据库中的土壤成分，将其作为贝叶斯条件自回归协变量，以确定与小区域 ALS 风险的关联。
方法
从国家土壤数据库（通过爱尔兰 EPA）获得了 45 种不同土壤参数的数据。我们使用普通克里金法为每个小区域内的每个土壤成分插值平均值。从爱尔兰 ALS 登记处获得了 1995 年 1 月至 2013 年 12 月期间爱尔兰所有 ALS 病例，并计算了每个 SA 的观察和年龄性别标准化预期病例。然后，为每个 ALS 亚型分别构建包括国家土壤数据库中每个参数的 Besag-York-Mollié（BYM）模型，作为 BYM 模型中的贝叶斯协变量。使用偏差信息准则（DIC）比较模型，并为 ALS 亚型分别构建单独的模型。
结果
共纳入 1701 例 ALS 患者，其中 959 例（56%）为男性，938 例（55%）为肢体起病 ALS。总共构建了 315 个贝叶斯模型。在所构建的 315 个模型中，只有一个模型的系数不与零重叠。对于肢体起病病例，总镁的平均系数为 0.319（可信区间 0.033-0.607）。
讨论
我们报告了第一个使用基于人群的数据集进行的 ALS 与土壤矿物质之间潜在关联的空间分析，该数据集收集了 18 年的数据。我们唯一的非零发现很可能是由于构建的模型数量众多而导致的随机发现。我们没有发现任何证据支持土壤矿物质和毒素水平是 ALS 的危险因素。然而，由于土壤参数是特定区域暴露的生态评估，因此需要进行个体水平的暴露测量。
Zotero 插件